Sharma, NK;
Pedreira, C;
Centeno, M;
Chaudhary, UJ;
Wehner, T;
Franca, LGS;
Yadee, T;
... Lemieux, L; + view all
(2017)
A novel scheme for the validation of an automated classification method for epileptic spikes by comparison with multiple observers.
Clinical Neurophysiology
, 128
(7)
pp. 1246-1254.
10.1016/j.clinph.2017.04.016.
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Abstract
OBJECTIVE: To validate the application of an automated neuronal spike classification algorithm, Wave_clus (WC), on interictal epileptiform discharges (IED) obtained from human intracranial EEG (icEEG) data. METHOD: Five 10-min segments of icEEG recorded in 5 patients were used. WC and three expert EEG reviewers independently classified one hundred IED events into IED classes or non-IEDs. First, we determined whether WC-human agreement variability falls within inter-reviewer agreement variability by calculating the variation of information for each classifier pair and quantifying the overlap between all WC-reviewer and all reviewer-reviewer pairs. Second, we compared WC and EEG reviewers’ spike identification and individual spike class labels visually and quantitatively. RESULTS: The overlap between all WC-human pairs and all human pairs was >80% for 3/5 patients and >58% for the other 2 patients demonstrating WC falling within inter-human variation. The average sensitivity of spike marking for WC was 91% and >87% for all three EEG reviewers. Finally, there was a strong visual and quantitative similarity between WC and EEG reviewers. CONCLUSIONS: WC performance is indistinguishable to that of EEG reviewers’ suggesting it could be a valid clinical tool for the assessment of IEDs. SIGNIFICANCE: WC can be used to provide quantitative analysis of epileptic spikes.
| Type: | Article |
|---|---|
| Title: | A novel scheme for the validation of an automated classification method for epileptic spikes by comparison with multiple observers |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1016/j.clinph.2017.04.016 |
| Publisher version: | http://doi.org/10.1016/j.clinph.2017.04.016 |
| Language: | English |
| Additional information: | © 2017 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
| Keywords: | Science & Technology, Life Sciences & Biomedicine, Clinical Neurology, Neurosciences, Neurosciences & Neurology, Interictal spike classification, Intracranial EEG, Automated spike classification, Information theory, INTRACRANIAL EEG-FMRI, INTERICTAL SPIKES, SEIZURE ONSET, RECORDINGS, DISCHARGES, HUMANS, SIGNAL, ALGORITHM, IDENTIFY, DISTANCE |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology > Clinical and Experimental Epilepsy UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Med Phys and Biomedical Eng |
| URI: | https://discovery.ucl.ac.uk/id/eprint/1554766 |
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